Vector Databases for AI Apps is a structured, practical course covering Pinecone, Weaviate, pgvector, RAG, Embeddings. It emphasizes explainable methods, checked work and a final outcome that can be reviewed.
DatabasesTool stack
PostgreSQLLangChain
Technology marks for Vector Databases for AI Apps, sourced from the CC0-licensed Simple Icons project.
Use Pinecone appropriately in a realistic, bounded task.
Use Weaviate appropriately in a realistic, bounded task.
Use pgvector appropriately in a realistic, bounded task.
Use RAG appropriately in a realistic, bounded task.
Course outline and revision prompts
Module 1
Pinecone: Data modelling
Apply Pinecone through entities, relationships, constraints.
Lesson
Key terms
Revision question
Entities with Pinecone
entities, Pinecone, databases
In Vector Databases for AI Apps, which evidence best supports a entities result produced with Pinecone?
Relationships with Weaviate
relationships, Weaviate, databases
In Vector Databases for AI Apps, which evidence best supports a relationships result produced with Weaviate?
Constraints with pgvector
constraints, pgvector, databases
In Vector Databases for AI Apps, which evidence best supports a constraints result produced with pgvector?
Instructional figureNested structure
Entities: from context to evidence
Entities connects business rule and records to a valid data state in Vector Databases for AI Apps.
1Business rule and records
Define the purpose, intended user and Pinecone constraints.
frames
2Entities
Derive entities from business rules and define stable identifiers.
produces evidence for
3Valid data state
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Entities is credible only when the result can be traced back to its purpose, inputs and constraints. Entities is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 2
Weaviate: Querying
Apply Weaviate through selection, joins, aggregation.
Lesson
Key terms
Revision question
Selection with RAG
selection, RAG, databases
In Vector Databases for AI Apps, which evidence best supports a selection result produced with RAG?
Joins with Embeddings
joins, Embeddings, databases
In Vector Databases for AI Apps, which evidence best supports a joins result produced with Embeddings?
Aggregation with Pinecone
aggregation, Pinecone, databases
In Vector Databases for AI Apps, which evidence best supports a aggregation result produced with Pinecone?
Instructional figureContinuous cycle
Selection: from context to evidence
Selection connects business rule and records to a valid data state in Vector Databases for AI Apps.
1Business rule and records
Define the purpose, intended user and RAG constraints.
frames
2Selection
Translate conditions into predicates and test boundaries.
produces evidence for
3Valid data state
Compare the observed result with a normal case, boundary case and stated limitation.
Review the evidence, adjust the method, and repeat.
Takeaway: Selection is credible only when the result can be traced back to its purpose, inputs and constraints. Selection is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 3
pgvector: Reliability
Apply pgvector through transactions, indexes, backups.
Lesson
Key terms
Revision question
Transactions with Weaviate
transactions, Weaviate, databases
In Vector Databases for AI Apps, which evidence best supports a transactions result produced with Weaviate?
Indexes with pgvector
indexes, pgvector, databases
In Vector Databases for AI Apps, which evidence best supports a indexes result produced with pgvector?
Backups with RAG
backups, RAG, databases
In Vector Databases for AI Apps, which evidence best supports a backups result produced with RAG?
Instructional figureProcess flow
Transactions: from context to evidence
Transactions connects business rule and records to a valid data state in Vector Databases for AI Apps.
1Business rule and records
Define the purpose, intended user and Weaviate constraints.
frames
2Transactions
Choose boundaries that preserve invariants under failure.
produces evidence for
3Valid data state
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Transactions is credible only when the result can be traced back to its purpose, inputs and constraints. Transactions is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 4
RAG: Production design
Apply RAG through security, performance, operations.
Lesson
Key terms
Revision question
Security with Embeddings
security, Embeddings, databases
In Vector Databases for AI Apps, which evidence best supports a security result produced with Embeddings?
Performance with Pinecone
performance, Pinecone, databases
In Vector Databases for AI Apps, which evidence best supports a performance result produced with Pinecone?
Operations with Weaviate
operations, Weaviate, databases
In Vector Databases for AI Apps, which evidence best supports a operations result produced with Weaviate?
Instructional figureContinuous cycle
Security: from context to evidence
Security connects business rule and records to a valid data state in Vector Databases for AI Apps.
1Business rule and records
Define the purpose, intended user and Embeddings constraints.
frames
2Security
Grant roles by task and test prohibited operations.
produces evidence for
3Valid data state
Compare the observed result with a normal case, boundary case and stated limitation.
Review the evidence, adjust the method, and repeat.
Takeaway: Security is credible only when the result can be traced back to its purpose, inputs and constraints. Security is the decision layer between the starting context and evidence that the result is fit for purpose.
Course practical outcome
Produce a reviewable Vector Databases for AI Apps project using Pinecone, Weaviate, pgvector.
Expected output: A working Vector Databases for AI Apps artefact plus an evidence-based self-review.
Tools: A suitable Pinecone environment, A plain-text decision log, Test data or realistic sample material
Production steps
Define the intended user, outcome and constraints.
Create the smallest complete result using Pinecone.
Test one normal case, one boundary case and one failure response.
Revise the work from the evidence and preserve before-and-after results.
Prepare a concise handover containing method, limitations and next step.
Success criteria
The output matches the stated outcome.
Inputs and decisions are reproducible.
Boundary and failure evidence is included.
Limitations and responsibility considerations are explicit.
Self-review
Can another learner repeat the method?
Did I test a difficult case?
Did I avoid unsupported claims?
Is the next action proportionate to the remaining risk?
Next step: Choose one weakness found during review and improve it before extending the Pinecone scope.
Glossary
Pinecone
A core concept or tool used in Vector Databases for AI Apps; its exact meaning is established in the relevant lesson.
Weaviate
A core concept or tool used in Vector Databases for AI Apps; its exact meaning is established in the relevant lesson.
pgvector
A core concept or tool used in Vector Databases for AI Apps; its exact meaning is established in the relevant lesson.
RAG
A core concept or tool used in Vector Databases for AI Apps; its exact meaning is established in the relevant lesson.
Embeddings
A core concept or tool used in Vector Databases for AI Apps; its exact meaning is established in the relevant lesson.
References
PostgreSQL Tutorial - PostgreSQL Global Development Group (accessed 2026-08-21)
SQL Language - PostgreSQL Global Development Group (accessed 2026-08-21)
This guide is generated from DigiLearn course material. Product versions, regulations and professional standards can change; consult the linked authoritative source before applying version-sensitive guidance.